A well-respected, stable financial services organization based in Los Angeles, CA is seeking an AI Engineer to design and implement next-generation Generative AI solutions that power real-world business applications. This role focuses on building RAG (Retrieval-Augmented Generation) pipelines, vector databases, and multi-modal AI systems that transform unstructured data into scalable, intelligent tools.
The ideal candidate will be a hands-on builder with experience developing AI pipelines and models in production environments - ideally coming out of Big Tech or top-tier AI teams - and comfortable leveraging modern AI frameworks and orchestration tools to deliver production-grade solutions.
What You'll Do
• Build and deploy RAG pipelines, AI agents, and multi-modal applications using frameworks such as LangChain, LangGraph, or similar orchestration tools.
• Design and maintain vector databases and data ingestion pipelines to move unstructured data into embedding-based storage for retrieval and modeling.
• Develop, fine-tune, and integrate LLMs and Generative AI models into production workflows and business systems.
• Collaborate cross-functionally with engineers, data scientists, and product teams to design and deliver end-to-end AI solutions.
• Optimize AI model performance, scalability, and cost in cloud environments (AWS, Azure, or GCP).
• Document system designs, workflows, and technical processes to ensure scalability and reproducibility.
• Stay current with emerging GenAI technologies, frameworks, and best practices.
What We're Looking For
• 5-7 years of experience in software or AI engineering, with at least 3+ years focused on machine learning, LLMs, or applied AI.
• Strong programming skills in Python with experience using frameworks such as PyTorch, TensorFlow, or similar.
• Hands-on experience working with tools such as HuggingFace, LangChain/LangGraph, n8n, or comparable orchestration frameworks (specific tools not mandatory).
• Proven experience building RAG systems and integrating LLMs with external or enterprise data sources.
• Background in
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